ANCOVA in flow (reading)
(flow (reading))
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
flow (reading) (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in flow (reading) (measured using pre- and post-tests).
Setting Initial Variables
dv = "flow.read"
dv.pos = "fss.media.read"
dv.pre = "dfs.media.read"
fatores2 <- c("Sexo","Zona","Cor.Raca","Serie")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["Sexo"]] = c("#FF007F","#4D4DFF")
color[["Zona"]] = c("#AA00FF","#00CCCC")
color[["Cor.Raca"]] = c(
"Parda"="#b97100","Indígena"="#9F262F",
"Branca"="#87c498", "Preta"="#848283","Amarela"="#D6B91C"
)
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["Sexo"]] = c("F","M")
level[["Zona"]] = c("Rural","Urbana")
level[["Cor.Raca"]] = c("Parda","Indígena","Branca", "Preta","Amarela")
level[["Serie"]] = c("6 ano","7 ano","8 ano","9 ano")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:Sexo"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:Zona"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:Cor.Raca"]] = c(
"Controle:Parda"="#e3c699", "Experimental:Parda"="#b97100",
"Controle:Indígena"="#e2bdc0", "Experimental:Indígena"="#9F262F",
"Controle:Branca"="#c0e8cb", "Experimental:Branca"="#87c498",
"Controle:Preta"="#dad9d9", "Experimental:Preta"="#848283",
"Controle:Amarela"="#eee3a4", "Experimental:Amarela"="#D6B91C",
"Controle.Parda"="#e3c699", "Experimental.Parda"="#b97100",
"Controle.Indígena"="#e2bdc0", "Experimental.Indígena"="#9F262F",
"Controle.Branca"="#c0e8cb", "Experimental.Branca"="#87c498",
"Controle.Preta"="#dad9d9", "Experimental.Preta"="#848283",
"Controle.Amarela"="#eee3a4", "Experimental.Amarela"="#D6B91C"
)
for (coln in c("vocab","vocab.teach","vocab.non.teach","score.tde",
"TFL.lidas.per.min","TFL.corretas.per.min","TFL.erradas.per.min","TFL.omitidas.per.min",
"leitura.compreensao")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "sumary")
gdat <- gdat[which(is.na(gdat$Necessidade.Deficiencia) & !is.na(gdat$Stari.Grupo)),]
dat <- gdat
dat$grupo <- factor(dat[["Stari.Grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
## There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
dfs.media.read |
101 |
3.303 |
3.333 |
2.111 |
4.444 |
0.455 |
0.045 |
0.090 |
0.556 |
YES |
-0.085 |
0.067 |
| Experimental |
|
|
|
|
dfs.media.read |
46 |
3.417 |
3.444 |
2.111 |
4.333 |
0.549 |
0.081 |
0.163 |
0.778 |
YES |
-0.263 |
-0.596 |
|
|
|
|
|
dfs.media.read |
147 |
3.339 |
3.333 |
2.111 |
4.444 |
0.487 |
0.040 |
0.079 |
0.667 |
YES |
-0.108 |
-0.170 |
| Controle |
|
|
|
|
fss.media.read |
101 |
3.483 |
3.444 |
2.222 |
5.000 |
0.498 |
0.050 |
0.098 |
0.556 |
YES |
0.312 |
0.491 |
| Experimental |
|
|
|
|
fss.media.read |
46 |
3.457 |
3.500 |
1.889 |
4.667 |
0.575 |
0.085 |
0.171 |
0.972 |
YES |
-0.247 |
-0.306 |
|
|
|
|
|
fss.media.read |
147 |
3.475 |
3.444 |
1.889 |
5.000 |
0.521 |
0.043 |
0.085 |
0.667 |
YES |
0.073 |
0.271 |
| Controle |
F |
|
|
|
dfs.media.read |
45 |
3.254 |
3.222 |
2.333 |
4.444 |
0.436 |
0.065 |
0.131 |
0.556 |
YES |
-0.005 |
0.272 |
| Controle |
M |
|
|
|
dfs.media.read |
56 |
3.344 |
3.333 |
2.111 |
4.333 |
0.469 |
0.063 |
0.126 |
0.556 |
YES |
-0.172 |
-0.114 |
| Experimental |
F |
|
|
|
dfs.media.read |
17 |
3.203 |
3.111 |
2.111 |
4.333 |
0.597 |
0.145 |
0.307 |
0.667 |
YES |
0.264 |
-0.541 |
| Experimental |
M |
|
|
|
dfs.media.read |
29 |
3.542 |
3.556 |
2.333 |
4.333 |
0.486 |
0.090 |
0.185 |
0.556 |
NO |
-0.501 |
-0.447 |
| Controle |
F |
|
|
|
fss.media.read |
45 |
3.500 |
3.444 |
2.444 |
4.778 |
0.481 |
0.072 |
0.145 |
0.444 |
NO |
0.529 |
0.150 |
| Controle |
M |
|
|
|
fss.media.read |
56 |
3.469 |
3.500 |
2.222 |
5.000 |
0.515 |
0.069 |
0.138 |
0.583 |
YES |
0.172 |
0.540 |
| Experimental |
F |
|
|
|
fss.media.read |
17 |
3.439 |
3.556 |
2.444 |
4.125 |
0.568 |
0.138 |
0.292 |
1.000 |
YES |
-0.225 |
-1.571 |
| Experimental |
M |
|
|
|
fss.media.read |
29 |
3.467 |
3.444 |
1.889 |
4.667 |
0.589 |
0.109 |
0.224 |
0.778 |
YES |
-0.252 |
0.160 |
| Controle |
|
Rural |
|
|
dfs.media.read |
55 |
3.307 |
3.333 |
2.111 |
4.444 |
0.549 |
0.074 |
0.148 |
0.722 |
YES |
-0.010 |
-0.602 |
| Controle |
|
Urbana |
|
|
dfs.media.read |
18 |
3.241 |
3.333 |
2.556 |
3.778 |
0.380 |
0.089 |
0.189 |
0.500 |
NO |
-0.652 |
-0.810 |
| Controle |
|
|
|
|
dfs.media.read |
28 |
3.338 |
3.278 |
2.889 |
3.778 |
0.262 |
0.050 |
0.102 |
0.434 |
YES |
0.088 |
-1.320 |
| Experimental |
|
Rural |
|
|
dfs.media.read |
29 |
3.531 |
3.556 |
2.333 |
4.333 |
0.550 |
0.102 |
0.209 |
0.778 |
YES |
-0.368 |
-0.689 |
| Experimental |
|
Urbana |
|
|
dfs.media.read |
8 |
3.042 |
3.167 |
2.111 |
3.556 |
0.493 |
0.174 |
0.412 |
0.472 |
NO |
-0.699 |
-1.063 |
| Experimental |
|
|
|
|
dfs.media.read |
9 |
3.383 |
3.333 |
2.778 |
4.000 |
0.481 |
0.160 |
0.370 |
0.778 |
YES |
0.034 |
-1.975 |
| Controle |
|
Rural |
|
|
fss.media.read |
55 |
3.528 |
3.444 |
2.556 |
4.778 |
0.446 |
0.060 |
0.121 |
0.500 |
NO |
0.529 |
0.255 |
| Controle |
|
Urbana |
|
|
fss.media.read |
18 |
3.420 |
3.278 |
2.222 |
5.000 |
0.769 |
0.181 |
0.382 |
1.122 |
YES |
0.372 |
-0.958 |
| Controle |
|
|
|
|
fss.media.read |
28 |
3.435 |
3.500 |
2.500 |
4.000 |
0.373 |
0.070 |
0.145 |
0.472 |
NO |
-0.515 |
-0.269 |
| Experimental |
|
Rural |
|
|
fss.media.read |
29 |
3.472 |
3.556 |
1.889 |
4.667 |
0.630 |
0.117 |
0.239 |
0.889 |
YES |
-0.393 |
-0.270 |
| Experimental |
|
Urbana |
|
|
fss.media.read |
8 |
3.431 |
3.444 |
2.778 |
4.111 |
0.509 |
0.180 |
0.425 |
0.861 |
YES |
0.024 |
-1.815 |
| Experimental |
|
|
|
|
fss.media.read |
9 |
3.432 |
3.222 |
3.000 |
4.222 |
0.495 |
0.165 |
0.381 |
0.889 |
YES |
0.404 |
-1.745 |
| Controle |
|
|
Parda |
|
dfs.media.read |
46 |
3.451 |
3.500 |
2.444 |
4.444 |
0.457 |
0.067 |
0.136 |
0.444 |
YES |
-0.028 |
-0.392 |
| Controle |
|
|
Branca |
|
dfs.media.read |
11 |
3.202 |
3.333 |
2.111 |
4.222 |
0.618 |
0.186 |
0.415 |
0.500 |
YES |
-0.228 |
-0.922 |
| Controle |
|
|
Preta |
|
dfs.media.read |
1 |
3.111 |
3.111 |
3.111 |
3.111 |
|
|
|
0.000 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
dfs.media.read |
43 |
3.176 |
3.222 |
2.333 |
3.778 |
0.365 |
0.056 |
0.112 |
0.444 |
YES |
-0.467 |
-0.445 |
| Experimental |
|
|
Parda |
|
dfs.media.read |
12 |
3.479 |
3.444 |
2.444 |
4.333 |
0.523 |
0.151 |
0.332 |
0.618 |
YES |
-0.146 |
-0.774 |
| Experimental |
|
|
Indígena |
|
dfs.media.read |
5 |
3.644 |
3.556 |
3.444 |
4.000 |
0.214 |
0.096 |
0.265 |
0.111 |
NO |
0.728 |
-1.303 |
| Experimental |
|
|
Branca |
|
dfs.media.read |
6 |
3.611 |
3.778 |
2.778 |
4.333 |
0.599 |
0.245 |
0.629 |
0.750 |
YES |
-0.280 |
-1.814 |
| Experimental |
|
|
Amarela |
|
dfs.media.read |
1 |
3.000 |
3.000 |
3.000 |
3.000 |
|
|
|
0.000 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
dfs.media.read |
22 |
3.297 |
3.333 |
2.111 |
4.333 |
0.599 |
0.128 |
0.266 |
0.882 |
YES |
-0.093 |
-0.902 |
| Controle |
|
|
Parda |
|
fss.media.read |
46 |
3.514 |
3.444 |
2.444 |
5.000 |
0.530 |
0.078 |
0.157 |
0.628 |
NO |
0.545 |
0.343 |
| Controle |
|
|
Branca |
|
fss.media.read |
11 |
3.596 |
3.556 |
2.667 |
4.556 |
0.505 |
0.152 |
0.339 |
0.333 |
YES |
0.140 |
-0.470 |
| Controle |
|
|
Preta |
|
fss.media.read |
1 |
3.444 |
3.444 |
3.444 |
3.444 |
|
|
|
0.000 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
|
fss.media.read |
43 |
3.421 |
3.444 |
2.222 |
4.556 |
0.469 |
0.072 |
0.144 |
0.500 |
YES |
-0.091 |
0.156 |
| Experimental |
|
|
Parda |
|
fss.media.read |
12 |
3.278 |
3.278 |
1.889 |
4.444 |
0.650 |
0.188 |
0.413 |
0.722 |
YES |
-0.325 |
-0.255 |
| Experimental |
|
|
Indígena |
|
fss.media.read |
5 |
3.647 |
4.000 |
2.444 |
4.222 |
0.737 |
0.329 |
0.915 |
0.681 |
NO |
-0.703 |
-1.458 |
| Experimental |
|
|
Branca |
|
fss.media.read |
6 |
3.556 |
3.556 |
3.000 |
4.222 |
0.410 |
0.167 |
0.430 |
0.306 |
YES |
0.279 |
-1.252 |
| Experimental |
|
|
Amarela |
|
fss.media.read |
1 |
3.222 |
3.222 |
3.222 |
3.222 |
|
|
|
0.000 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
|
fss.media.read |
22 |
3.495 |
3.556 |
2.667 |
4.667 |
0.557 |
0.119 |
0.247 |
1.000 |
YES |
0.133 |
-1.212 |
| Controle |
|
|
|
6 ano |
dfs.media.read |
32 |
3.156 |
3.222 |
2.111 |
4.444 |
0.561 |
0.099 |
0.202 |
0.750 |
YES |
0.203 |
-0.450 |
| Controle |
|
|
|
7 ano |
dfs.media.read |
33 |
3.420 |
3.444 |
2.750 |
4.000 |
0.302 |
0.053 |
0.107 |
0.444 |
YES |
-0.315 |
-0.571 |
| Controle |
|
|
|
8 ano |
dfs.media.read |
17 |
3.268 |
3.222 |
2.556 |
4.333 |
0.445 |
0.108 |
0.229 |
0.556 |
NO |
0.613 |
-0.214 |
| Controle |
|
|
|
9 ano |
dfs.media.read |
19 |
3.381 |
3.333 |
2.444 |
4.222 |
0.444 |
0.102 |
0.214 |
0.500 |
YES |
0.152 |
-0.548 |
| Experimental |
|
|
|
6 ano |
dfs.media.read |
15 |
3.619 |
3.444 |
3.000 |
4.333 |
0.453 |
0.117 |
0.251 |
0.722 |
YES |
0.144 |
-1.548 |
| Experimental |
|
|
|
7 ano |
dfs.media.read |
11 |
3.485 |
3.444 |
2.778 |
4.333 |
0.571 |
0.172 |
0.384 |
0.944 |
YES |
0.238 |
-1.575 |
| Experimental |
|
|
|
8 ano |
dfs.media.read |
11 |
3.121 |
3.333 |
2.111 |
4.000 |
0.656 |
0.198 |
0.441 |
1.111 |
YES |
-0.222 |
-1.662 |
| Experimental |
|
|
|
9 ano |
dfs.media.read |
9 |
3.358 |
3.556 |
2.778 |
3.889 |
0.426 |
0.142 |
0.327 |
0.667 |
YES |
-0.246 |
-1.764 |
| Controle |
|
|
|
6 ano |
fss.media.read |
32 |
3.521 |
3.556 |
2.222 |
4.556 |
0.465 |
0.082 |
0.168 |
0.361 |
YES |
-0.132 |
0.785 |
| Controle |
|
|
|
7 ano |
fss.media.read |
33 |
3.546 |
3.444 |
2.444 |
5.000 |
0.573 |
0.100 |
0.203 |
0.653 |
NO |
0.567 |
0.138 |
| Controle |
|
|
|
8 ano |
fss.media.read |
17 |
3.273 |
3.333 |
2.500 |
4.000 |
0.477 |
0.116 |
0.245 |
0.556 |
YES |
0.026 |
-1.123 |
| Controle |
|
|
|
9 ano |
fss.media.read |
19 |
3.497 |
3.444 |
2.778 |
4.222 |
0.410 |
0.094 |
0.197 |
0.444 |
YES |
0.234 |
-0.930 |
| Experimental |
|
|
|
6 ano |
fss.media.read |
15 |
3.674 |
3.667 |
3.000 |
4.667 |
0.469 |
0.121 |
0.259 |
0.722 |
YES |
0.255 |
-0.900 |
| Experimental |
|
|
|
7 ano |
fss.media.read |
11 |
3.395 |
3.556 |
1.889 |
4.444 |
0.808 |
0.244 |
0.543 |
1.174 |
YES |
-0.427 |
-1.227 |
| Experimental |
|
|
|
8 ano |
fss.media.read |
11 |
3.222 |
3.000 |
2.778 |
4.222 |
0.463 |
0.140 |
0.311 |
0.556 |
NO |
0.857 |
-0.606 |
| Experimental |
|
|
|
9 ano |
fss.media.read |
9 |
3.457 |
3.556 |
2.667 |
4.000 |
0.473 |
0.158 |
0.363 |
0.778 |
YES |
-0.336 |
-1.525 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "fss.media.read", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.read"]] <- c(pdat[["dfs.media.read"]], pdat[["fss.media.read"]])
aov = anova_test(pdat, fss.media.read ~ dfs.media.read + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, fss.media.read ~ grupo, covariate = dfs.media.read,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "fss.media.read", "grupo", covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.dfs.media.read","se.dfs.media.read","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(fss.media.read ~ dfs.media.read + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.read"]] <- c(wdat[["dfs.media.read"]], wdat[["fss.media.read"]])
ldat[["grupo"]] = wdat
(non.normal)
## NULL
aov = anova_test(wdat, fss.media.read ~ dfs.media.read + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 dfs.media.read 1 144 16.860 6.72e-05 * 0.105
## 2 grupo 1 144 0.542 4.63e-01 0.004
| dfs.media.read |
1 |
144 |
16.860 |
0.000 |
* |
0.105 |
| grupo |
1 |
144 |
0.542 |
0.463 |
|
0.004 |
pwc <- emmeans_test(wdat, fss.media.read ~ grupo, covariate = dfs.media.read,
p.adjust.method = "bonferroni")
| dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
144 |
0.737 |
0.463 |
0.463 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
flow.read |
pre |
pos |
290 |
-2.525 |
0.012 |
0.012 |
* |
| Experimental |
time |
flow.read |
pre |
pos |
290 |
-0.382 |
0.703 |
0.703 |
ns |
ds <- get.descriptives(wdat, "fss.media.read", "grupo", covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.dfs.media.read","se.dfs.media.read","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
101 |
3.303 |
0.045 |
3.483 |
0.050 |
3.495 |
0.050 |
3.397 |
3.593 |
| Experimental |
46 |
3.417 |
0.081 |
3.457 |
0.085 |
3.430 |
0.074 |
3.284 |
3.575 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "fss.media.read", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "fss.media.read", "grupo", aov, pwc, covar = "dfs.media.read",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("flow (reading)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "flow.read", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("flow (reading)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(fss.media.read ~ dfs.media.read + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.996 0.951
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 145 1.24 0.267
ANCOVA and
Pairwise for two factors grupo:Sexo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Sexo"]]),],
"fss.media.read", c("grupo","Sexo"))
pdat = pdat[pdat[["Sexo"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Sexo"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Sexo"]] = factor(
pdat[["Sexo"]],
level[["Sexo"]][level[["Sexo"]] %in% unique(pdat[["Sexo"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Sexo")], pdat[,c("id","grupo","Sexo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.read"]] <- c(pdat[["dfs.media.read"]], pdat[["fss.media.read"]])
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(pdat, fss.media.read ~ dfs.media.read + grupo*Sexo)
laov[["grupo:Sexo"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(pdat, grupo), fss.media.read ~ Sexo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Sexo), fss.media.read ~ grupo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Sexo")),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.media.read", c("grupo","Sexo"), covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.dfs.media.read","se.dfs.media.read","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Sexo"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.media.read ~ dfs.media.read + grupo*Sexo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Sexo")], wdat[,c("id","grupo","Sexo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.read"]] <- c(wdat[["dfs.media.read"]], wdat[["fss.media.read"]])
ldat[["grupo:Sexo"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Sexo"]])) >= 2) {
aov = anova_test(wdat, fss.media.read ~ dfs.media.read + grupo*Sexo)
laov[["grupo:Sexo"]] <- merge(get_anova_table(aov), laov[["grupo:Sexo"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.media.read |
1 |
142 |
17.341 |
0.000 |
* |
0.109 |
| grupo |
1 |
142 |
0.471 |
0.493 |
|
0.003 |
| Sexo |
1 |
142 |
0.726 |
0.396 |
|
0.005 |
| grupo:Sexo |
1 |
142 |
0.029 |
0.864 |
|
0.000 |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwcs <- list()
pwcs[["Sexo"]] <- emmeans_test(
group_by(wdat, grupo), fss.media.read ~ Sexo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Sexo), fss.media.read ~ grupo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Sexo"]])
pwc <- pwc[,c("grupo","Sexo", colnames(pwc)[!colnames(pwc) %in% c("grupo","Sexo")])]
}
|
F |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
142 |
0.299 |
0.766 |
0.766 |
ns |
|
M |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
142 |
0.640 |
0.523 |
0.523 |
ns |
| Controle |
|
dfs.media.read*Sexo |
fss.media.read |
F |
M |
142 |
0.629 |
0.530 |
0.530 |
ns |
| Experimental |
|
dfs.media.read*Sexo |
fss.media.read |
F |
M |
142 |
0.609 |
0.544 |
0.544 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Sexo")),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Sexo"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Sexo"]],
by=c("grupo","Sexo","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
flow.read |
pre |
pos |
286 |
-2.319 |
0.021 |
0.021 |
* |
| Controle |
M |
time |
flow.read |
pre |
pos |
286 |
-1.322 |
0.187 |
0.187 |
ns |
| Experimental |
F |
time |
flow.read |
pre |
pos |
286 |
-1.368 |
0.172 |
0.172 |
ns |
| Experimental |
M |
time |
flow.read |
pre |
pos |
286 |
0.565 |
0.572 |
0.572 |
ns |
if (length(unique(pdat[["Sexo"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.media.read", c("grupo","Sexo"), covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = c("grupo","Sexo"), all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Sexo"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Sexo","n","mean.dfs.media.read","se.dfs.media.read",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Sexo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Sexo"]] <- merge(ds, lemms[["grupo:Sexo"]],
by=c("grupo","Sexo"), suffixes = c("","'"))
}
| Controle |
F |
45 |
3.254 |
0.065 |
3.500 |
0.072 |
3.531 |
0.075 |
3.383 |
3.678 |
| Controle |
M |
56 |
3.344 |
0.063 |
3.469 |
0.069 |
3.468 |
0.067 |
3.336 |
3.599 |
| Experimental |
F |
17 |
3.203 |
0.145 |
3.439 |
0.138 |
3.488 |
0.122 |
3.248 |
3.728 |
| Experimental |
M |
29 |
3.542 |
0.090 |
3.467 |
0.109 |
3.394 |
0.094 |
3.207 |
3.580 |
Plots for ancova
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Sexo", aov, ylab = "flow (reading)",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggPlotAoC2(pwcs, "Sexo", "grupo", aov, ylab = "flow (reading)",
subtitle = which(aov$Effect == "grupo:Sexo"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.media.read", c("grupo","Sexo"), aov, pwcs, covar = "dfs.media.read",
theme = "classic", color = color[["grupo:Sexo"]],
subtitle = which(aov$Effect == "grupo:Sexo"))
}
if (length(unique(pdat[["Sexo"]])) >= 2) {
plots[["grupo:Sexo"]] + ggplot2::ylab("flow (reading)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Sexo"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.read", c("grupo","Sexo"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Sexo"]])) >= 2)
plots[["grupo:Sexo"]] + ggplot2::ylab("flow (reading)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
facet.by = c("grupo","Sexo"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "grupo", facet.by = "Sexo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Sexo"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "Sexo", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Sexo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Sexo"))) +
ggplot2::scale_color_manual(values = color[["Sexo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Sexo"]])) >= 2)
res <- augment(lm(fss.media.read ~ dfs.media.read + grupo*Sexo, data = wdat))
if (length(unique(pdat[["Sexo"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.996 0.956
if (length(unique(pdat[["Sexo"]])) >= 2)
levene_test(res, .resid ~ grupo*Sexo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 143 1.11 0.345
ANCOVA and
Pairwise for two factors grupo:Zona
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Zona"]]),],
"fss.media.read", c("grupo","Zona"))
pdat = pdat[pdat[["Zona"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Zona"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Zona"]] = factor(
pdat[["Zona"]],
level[["Zona"]][level[["Zona"]] %in% unique(pdat[["Zona"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Zona")], pdat[,c("id","grupo","Zona")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.read"]] <- c(pdat[["dfs.media.read"]], pdat[["fss.media.read"]])
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(pdat, fss.media.read ~ dfs.media.read + grupo*Zona)
laov[["grupo:Zona"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(pdat, grupo), fss.media.read ~ Zona,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Zona), fss.media.read ~ grupo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Zona")),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.media.read", c("grupo","Zona"), covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.dfs.media.read","se.dfs.media.read","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Zona"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.media.read ~ dfs.media.read + grupo*Zona, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Zona")], wdat[,c("id","grupo","Zona")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.read"]] <- c(wdat[["dfs.media.read"]], wdat[["fss.media.read"]])
ldat[["grupo:Zona"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Zona"]])) >= 2) {
aov = anova_test(wdat, fss.media.read ~ dfs.media.read + grupo*Zona)
laov[["grupo:Zona"]] <- merge(get_anova_table(aov), laov[["grupo:Zona"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.media.read |
1 |
105 |
12.840 |
0.001 |
* |
0.109 |
| grupo |
1 |
105 |
0.615 |
0.435 |
|
0.006 |
| Zona |
1 |
105 |
0.023 |
0.879 |
|
0.000 |
| grupo:Zona |
1 |
105 |
0.694 |
0.407 |
|
0.007 |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwcs <- list()
pwcs[["Zona"]] <- emmeans_test(
group_by(wdat, grupo), fss.media.read ~ Zona,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Zona), fss.media.read ~ grupo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Zona"]])
pwc <- pwc[,c("grupo","Zona", colnames(pwc)[!colnames(pwc) %in% c("grupo","Zona")])]
}
|
Rural |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
105 |
1.092 |
0.277 |
0.277 |
ns |
|
Urbana |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
105 |
-0.359 |
0.721 |
0.721 |
ns |
| Controle |
|
dfs.media.read*Zona |
fss.media.read |
Rural |
Urbana |
105 |
0.584 |
0.561 |
0.561 |
ns |
| Experimental |
|
dfs.media.read*Zona |
fss.media.read |
Rural |
Urbana |
105 |
-0.608 |
0.545 |
0.545 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Zona")),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Zona"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Zona"]],
by=c("grupo","Zona","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
flow.read |
pre |
pos |
212 |
-2.138 |
0.034 |
0.034 |
* |
| Controle |
Urbana |
time |
flow.read |
pre |
pos |
212 |
-0.989 |
0.324 |
0.324 |
ns |
| Experimental |
Rural |
time |
flow.read |
pre |
pos |
212 |
0.413 |
0.680 |
0.680 |
ns |
| Experimental |
Urbana |
time |
flow.read |
pre |
pos |
212 |
-1.432 |
0.154 |
0.154 |
ns |
if (length(unique(pdat[["Zona"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.media.read", c("grupo","Zona"), covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = c("grupo","Zona"), all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Zona"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Zona","n","mean.dfs.media.read","se.dfs.media.read",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Zona", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Zona"]] <- merge(ds, lemms[["grupo:Zona"]],
by=c("grupo","Zona"), suffixes = c("","'"))
}
| Controle |
Rural |
55 |
3.307 |
0.074 |
3.528 |
0.060 |
3.538 |
0.072 |
3.395 |
3.681 |
| Controle |
Urbana |
18 |
3.241 |
0.089 |
3.420 |
0.181 |
3.454 |
0.126 |
3.203 |
3.704 |
| Experimental |
Rural |
29 |
3.531 |
0.102 |
3.472 |
0.117 |
3.402 |
0.101 |
3.202 |
3.603 |
| Experimental |
Urbana |
8 |
3.042 |
0.174 |
3.431 |
0.180 |
3.535 |
0.191 |
3.156 |
3.915 |
Plots for ancova
if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Zona", aov, ylab = "flow (reading)",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
ggPlotAoC2(pwcs, "Zona", "grupo", aov, ylab = "flow (reading)",
subtitle = which(aov$Effect == "grupo:Zona"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.media.read", c("grupo","Zona"), aov, pwcs, covar = "dfs.media.read",
theme = "classic", color = color[["grupo:Zona"]],
subtitle = which(aov$Effect == "grupo:Zona"))
}
if (length(unique(pdat[["Zona"]])) >= 2) {
plots[["grupo:Zona"]] + ggplot2::ylab("flow (reading)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Zona"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.read", c("grupo","Zona"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Zona"]])) >= 2)
plots[["grupo:Zona"]] + ggplot2::ylab("flow (reading)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
facet.by = c("grupo","Zona"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "grupo", facet.by = "Zona", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Zona"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "Zona", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Zona)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Zona"))) +
ggplot2::scale_color_manual(values = color[["Zona"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Zona"]])) >= 2)
res <- augment(lm(fss.media.read ~ dfs.media.read + grupo*Zona, data = wdat))
if (length(unique(pdat[["Zona"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.997 0.999
if (length(unique(pdat[["Zona"]])) >= 2)
levene_test(res, .resid ~ grupo*Zona)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 106 4.32 0.00645
ANCOVA and
Pairwise for two factors grupo:Cor.Raca
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Cor.Raca"]]),],
"fss.media.read", c("grupo","Cor.Raca"))
## Warning: There were 2 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
pdat = pdat[pdat[["Cor.Raca"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Cor.Raca"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Cor.Raca"]] = factor(
pdat[["Cor.Raca"]],
level[["Cor.Raca"]][level[["Cor.Raca"]] %in% unique(pdat[["Cor.Raca"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Cor.Raca")], pdat[,c("id","grupo","Cor.Raca")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.read"]] <- c(pdat[["dfs.media.read"]], pdat[["fss.media.read"]])
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(pdat, fss.media.read ~ dfs.media.read + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(pdat, grupo), fss.media.read ~ Cor.Raca,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Cor.Raca), fss.media.read ~ grupo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Cor.Raca")),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.media.read", c("grupo","Cor.Raca"), covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.dfs.media.read","se.dfs.media.read","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.media.read ~ dfs.media.read + grupo*Cor.Raca, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Cor.Raca")], wdat[,c("id","grupo","Cor.Raca")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.read"]] <- c(wdat[["dfs.media.read"]], wdat[["fss.media.read"]])
ldat[["grupo:Cor.Raca"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
aov = anova_test(wdat, fss.media.read ~ dfs.media.read + grupo*Cor.Raca)
laov[["grupo:Cor.Raca"]] <- merge(get_anova_table(aov), laov[["grupo:Cor.Raca"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.media.read |
1 |
70 |
8.476 |
0.005 |
* |
0.108 |
| grupo |
1 |
70 |
2.632 |
0.109 |
|
0.036 |
| Cor.Raca |
1 |
70 |
1.724 |
0.193 |
|
0.024 |
| grupo:Cor.Raca |
1 |
70 |
0.040 |
0.843 |
|
0.001 |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwcs <- list()
pwcs[["Cor.Raca"]] <- emmeans_test(
group_by(wdat, grupo), fss.media.read ~ Cor.Raca,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Cor.Raca), fss.media.read ~ grupo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Cor.Raca"]])
pwc <- pwc[,c("grupo","Cor.Raca", colnames(pwc)[!colnames(pwc) %in% c("grupo","Cor.Raca")])]
}
|
Parda |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
70 |
1.482 |
0.143 |
0.143 |
ns |
|
Branca |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
70 |
0.696 |
0.489 |
0.489 |
ns |
| Controle |
|
dfs.media.read*Cor.Raca |
fss.media.read |
Parda |
Branca |
70 |
-0.967 |
0.337 |
0.337 |
ns |
| Experimental |
|
dfs.media.read*Cor.Raca |
fss.media.read |
Parda |
Branca |
70 |
-0.900 |
0.371 |
0.371 |
ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Cor.Raca")),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Cor.Raca"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Parda |
time |
flow.read |
pre |
pos |
142 |
-0.583 |
0.561 |
0.561 |
ns |
| Controle |
Branca |
time |
flow.read |
pre |
pos |
142 |
-1.770 |
0.079 |
0.079 |
ns |
| Experimental |
Parda |
time |
flow.read |
pre |
pos |
142 |
0.945 |
0.346 |
0.346 |
ns |
| Experimental |
Branca |
time |
flow.read |
pre |
pos |
142 |
0.184 |
0.854 |
0.854 |
ns |
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.media.read", c("grupo","Cor.Raca"), covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = c("grupo","Cor.Raca"), all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Cor.Raca"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Cor.Raca","n","mean.dfs.media.read","se.dfs.media.read",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Cor.Raca", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Cor.Raca"]] <- merge(ds, lemms[["grupo:Cor.Raca"]],
by=c("grupo","Cor.Raca"), suffixes = c("","'"))
}
| Controle |
Branca |
11 |
3.202 |
0.186 |
3.596 |
0.152 |
3.677 |
0.157 |
3.363 |
3.991 |
| Controle |
Parda |
46 |
3.451 |
0.067 |
3.514 |
0.078 |
3.508 |
0.076 |
3.357 |
3.659 |
| Experimental |
Branca |
6 |
3.611 |
0.245 |
3.556 |
0.167 |
3.492 |
0.211 |
3.072 |
3.913 |
| Experimental |
Parda |
12 |
3.479 |
0.151 |
3.278 |
0.188 |
3.261 |
0.148 |
2.965 |
3.557 |
Plots for ancova
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Cor.Raca", aov, ylab = "flow (reading)",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggPlotAoC2(pwcs, "Cor.Raca", "grupo", aov, ylab = "flow (reading)",
subtitle = which(aov$Effect == "grupo:Cor.Raca"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.media.read", c("grupo","Cor.Raca"), aov, pwcs, covar = "dfs.media.read",
theme = "classic", color = color[["grupo:Cor.Raca"]],
subtitle = which(aov$Effect == "grupo:Cor.Raca"))
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("flow (reading)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.read", c("grupo","Cor.Raca"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
plots[["grupo:Cor.Raca"]] + ggplot2::ylab("flow (reading)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
facet.by = c("grupo","Cor.Raca"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "grupo", facet.by = "Cor.Raca", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Cor.Raca"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "Cor.Raca", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Cor.Raca)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Cor.Raca"))) +
ggplot2::scale_color_manual(values = color[["Cor.Raca"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
res <- augment(lm(fss.media.read ~ dfs.media.read + grupo*Cor.Raca, data = wdat))
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.988 0.680
if (length(unique(pdat[["Cor.Raca"]])) >= 2)
levene_test(res, .resid ~ grupo*Cor.Raca)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 71 0.186 0.906
ANCOVA and
Pairwise for two factors grupo:Serie
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["Serie"]]),],
"fss.media.read", c("grupo","Serie"))
pdat = pdat[pdat[["Serie"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["Serie"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["Serie"]] = factor(
pdat[["Serie"]],
level[["Serie"]][level[["Serie"]] %in% unique(pdat[["Serie"]])])
pdat.long <- rbind(pdat[,c("id","grupo","Serie")], pdat[,c("id","grupo","Serie")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["flow.read"]] <- c(pdat[["dfs.media.read"]], pdat[["fss.media.read"]])
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(pdat, fss.media.read ~ dfs.media.read + grupo*Serie)
laov[["grupo:Serie"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(pdat, grupo), fss.media.read ~ Serie,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, Serie), fss.media.read ~ grupo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","Serie")),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(pdat, "fss.media.read", c("grupo","Serie"), covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.dfs.media.read","se.dfs.media.read","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["Serie"]])) >= 2) {
wdat = pdat
res = residuals(lm(fss.media.read ~ dfs.media.read + grupo*Serie, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","Serie")], wdat[,c("id","grupo","Serie")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["flow.read"]] <- c(wdat[["dfs.media.read"]], wdat[["fss.media.read"]])
ldat[["grupo:Serie"]] = wdat
(non.normal)
}
## NULL
if (length(unique(pdat[["Serie"]])) >= 2) {
aov = anova_test(wdat, fss.media.read ~ dfs.media.read + grupo*Serie)
laov[["grupo:Serie"]] <- merge(get_anova_table(aov), laov[["grupo:Serie"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| dfs.media.read |
1 |
138 |
13.891 |
0.000 |
* |
0.091 |
| grupo |
1 |
138 |
0.347 |
0.557 |
|
0.003 |
| Serie |
3 |
138 |
1.871 |
0.137 |
|
0.039 |
| grupo:Serie |
3 |
138 |
0.224 |
0.880 |
|
0.005 |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwcs <- list()
pwcs[["Serie"]] <- emmeans_test(
group_by(wdat, grupo), fss.media.read ~ Serie,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, Serie), fss.media.read ~ grupo,
covariate = dfs.media.read, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["Serie"]])
pwc <- pwc[,c("grupo","Serie", colnames(pwc)[!colnames(pwc) %in% c("grupo","Serie")])]
}
|
6 ano |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
138 |
-0.003 |
0.998 |
0.998 |
ns |
|
7 ano |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
138 |
0.996 |
0.321 |
0.321 |
ns |
|
8 ano |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
138 |
0.011 |
0.991 |
0.991 |
ns |
|
9 ano |
dfs.media.read*grupo |
fss.media.read |
Controle |
Experimental |
138 |
0.163 |
0.871 |
0.871 |
ns |
| Controle |
|
dfs.media.read*Serie |
fss.media.read |
6 ano |
7 ano |
138 |
0.496 |
0.621 |
1.000 |
ns |
| Controle |
|
dfs.media.read*Serie |
fss.media.read |
6 ano |
8 ano |
138 |
1.909 |
0.058 |
0.350 |
ns |
| Controle |
|
dfs.media.read*Serie |
fss.media.read |
6 ano |
9 ano |
138 |
0.676 |
0.500 |
1.000 |
ns |
| Controle |
|
dfs.media.read*Serie |
fss.media.read |
7 ano |
8 ano |
138 |
1.498 |
0.137 |
0.819 |
ns |
| Controle |
|
dfs.media.read*Serie |
fss.media.read |
7 ano |
9 ano |
138 |
0.251 |
0.802 |
1.000 |
ns |
| Controle |
|
dfs.media.read*Serie |
fss.media.read |
8 ano |
9 ano |
138 |
-1.126 |
0.262 |
1.000 |
ns |
| Experimental |
|
dfs.media.read*Serie |
fss.media.read |
6 ano |
7 ano |
138 |
1.190 |
0.236 |
1.000 |
ns |
| Experimental |
|
dfs.media.read*Serie |
fss.media.read |
6 ano |
8 ano |
138 |
1.424 |
0.157 |
0.940 |
ns |
| Experimental |
|
dfs.media.read*Serie |
fss.media.read |
6 ano |
9 ano |
138 |
0.623 |
0.534 |
1.000 |
ns |
| Experimental |
|
dfs.media.read*Serie |
fss.media.read |
7 ano |
8 ano |
138 |
0.247 |
0.806 |
1.000 |
ns |
| Experimental |
|
dfs.media.read*Serie |
fss.media.read |
7 ano |
9 ano |
138 |
-0.464 |
0.644 |
1.000 |
ns |
| Experimental |
|
dfs.media.read*Serie |
fss.media.read |
8 ano |
9 ano |
138 |
-0.698 |
0.486 |
1.000 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","Serie")),
flow.read ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:Serie"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:Serie"]],
by=c("grupo","Serie","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
6 ano |
time |
flow.read |
pre |
pos |
278 |
-2.934 |
0.004 |
0.004 |
** |
| Controle |
7 ano |
time |
flow.read |
pre |
pos |
278 |
-1.029 |
0.305 |
0.305 |
ns |
| Controle |
8 ano |
time |
flow.read |
pre |
pos |
278 |
-0.029 |
0.977 |
0.977 |
ns |
| Controle |
9 ano |
time |
flow.read |
pre |
pos |
278 |
-0.721 |
0.472 |
0.472 |
ns |
| Experimental |
6 ano |
time |
flow.read |
pre |
pos |
278 |
-0.306 |
0.760 |
0.760 |
ns |
| Experimental |
7 ano |
time |
flow.read |
pre |
pos |
278 |
0.423 |
0.673 |
0.673 |
ns |
| Experimental |
8 ano |
time |
flow.read |
pre |
pos |
278 |
-0.477 |
0.634 |
0.634 |
ns |
| Experimental |
9 ano |
time |
flow.read |
pre |
pos |
278 |
-0.422 |
0.674 |
0.674 |
ns |
if (length(unique(pdat[["Serie"]])) >= 2) {
ds <- get.descriptives(wdat, "fss.media.read", c("grupo","Serie"), covar = "dfs.media.read")
ds <- merge(ds[ds$variable != "dfs.media.read",],
ds[ds$variable == "dfs.media.read", !colnames(ds) %in% c("variable")],
by = c("grupo","Serie"), all.x = T, suffixes = c("", ".dfs.media.read"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","Serie"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","Serie","n","mean.dfs.media.read","se.dfs.media.read",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","Serie", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:Serie"]] <- merge(ds, lemms[["grupo:Serie"]],
by=c("grupo","Serie"), suffixes = c("","'"))
}
| Controle |
6 ano |
32 |
3.156 |
0.099 |
3.521 |
0.082 |
3.581 |
0.089 |
3.405 |
3.758 |
| Controle |
7 ano |
33 |
3.420 |
0.053 |
3.546 |
0.100 |
3.519 |
0.087 |
3.348 |
3.690 |
| Controle |
8 ano |
17 |
3.268 |
0.108 |
3.273 |
0.116 |
3.296 |
0.120 |
3.058 |
3.535 |
| Controle |
9 ano |
19 |
3.381 |
0.102 |
3.497 |
0.094 |
3.483 |
0.114 |
3.258 |
3.708 |
| Experimental |
6 ano |
15 |
3.619 |
0.117 |
3.674 |
0.121 |
3.582 |
0.130 |
3.324 |
3.840 |
| Experimental |
7 ano |
11 |
3.485 |
0.172 |
3.395 |
0.244 |
3.347 |
0.150 |
3.050 |
3.644 |
| Experimental |
8 ano |
11 |
3.121 |
0.198 |
3.222 |
0.140 |
3.294 |
0.151 |
2.996 |
3.592 |
| Experimental |
9 ano |
9 |
3.358 |
0.142 |
3.457 |
0.158 |
3.450 |
0.165 |
3.123 |
3.777 |
Plots for ancova
if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "Serie", aov, ylab = "flow (reading)",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
ggPlotAoC2(pwcs, "Serie", "grupo", aov, ylab = "flow (reading)",
subtitle = which(aov$Effect == "grupo:Serie"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "fss.media.read", c("grupo","Serie"), aov, pwcs, covar = "dfs.media.read",
theme = "classic", color = color[["grupo:Serie"]],
subtitle = which(aov$Effect == "grupo:Serie"))
}
if (length(unique(pdat[["Serie"]])) >= 2) {
plots[["grupo:Serie"]] + ggplot2::ylab("flow (reading)") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "flow.read", c("grupo","Serie"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["Serie"]])) >= 2)
plots[["grupo:Serie"]] + ggplot2::ylab("flow (reading)") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
facet.by = c("grupo","Serie"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "grupo", facet.by = "Serie", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["Serie"]])) >= 2) {
ggscatter(wdat, x = "dfs.media.read", y = "fss.media.read", size = 0.5,
color = "Serie", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = Serie)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:Serie"))) +
ggplot2::scale_color_manual(values = color[["Serie"]]) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["Serie"]])) >= 2)
res <- augment(lm(fss.media.read ~ dfs.media.read + grupo*Serie, data = wdat))
if (length(unique(pdat[["Serie"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.995 0.881
if (length(unique(pdat[["Serie"]])) >= 2)
levene_test(res, .resid ~ grupo*Serie)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 7 139 0.749 0.631
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
dfs.media.read |
101 |
3.303 |
3.333 |
2.111 |
4.444 |
0.455 |
0.045 |
0.090 |
0.556 |
YES |
-0.085 |
0.067 |
| Experimental |
|
|
|
|
dfs.media.read |
46 |
3.417 |
3.444 |
2.111 |
4.333 |
0.549 |
0.081 |
0.163 |
0.778 |
YES |
-0.263 |
-0.596 |
|
|
|
|
|
dfs.media.read |
147 |
3.339 |
3.333 |
2.111 |
4.444 |
0.487 |
0.040 |
0.079 |
0.667 |
YES |
-0.108 |
-0.170 |
| Controle |
|
|
|
|
fss.media.read |
101 |
3.483 |
3.444 |
2.222 |
5.000 |
0.498 |
0.050 |
0.098 |
0.556 |
YES |
0.312 |
0.491 |
| Experimental |
|
|
|
|
fss.media.read |
46 |
3.457 |
3.500 |
1.889 |
4.667 |
0.575 |
0.085 |
0.171 |
0.972 |
YES |
-0.247 |
-0.306 |
|
|
|
|
|
fss.media.read |
147 |
3.475 |
3.444 |
1.889 |
5.000 |
0.521 |
0.043 |
0.085 |
0.667 |
YES |
0.073 |
0.271 |
| Controle |
F |
|
|
|
dfs.media.read |
45 |
3.254 |
3.222 |
2.333 |
4.444 |
0.436 |
0.065 |
0.131 |
0.556 |
YES |
-0.005 |
0.272 |
| Controle |
M |
|
|
|
dfs.media.read |
56 |
3.344 |
3.333 |
2.111 |
4.333 |
0.469 |
0.063 |
0.126 |
0.556 |
YES |
-0.172 |
-0.114 |
| Experimental |
F |
|
|
|
dfs.media.read |
17 |
3.203 |
3.111 |
2.111 |
4.333 |
0.597 |
0.145 |
0.307 |
0.667 |
YES |
0.264 |
-0.541 |
| Experimental |
M |
|
|
|
dfs.media.read |
29 |
3.542 |
3.556 |
2.333 |
4.333 |
0.486 |
0.090 |
0.185 |
0.556 |
NO |
-0.501 |
-0.447 |
| Controle |
F |
|
|
|
fss.media.read |
45 |
3.500 |
3.444 |
2.444 |
4.778 |
0.481 |
0.072 |
0.145 |
0.444 |
NO |
0.529 |
0.150 |
| Controle |
M |
|
|
|
fss.media.read |
56 |
3.469 |
3.500 |
2.222 |
5.000 |
0.515 |
0.069 |
0.138 |
0.583 |
YES |
0.172 |
0.540 |
| Experimental |
F |
|
|
|
fss.media.read |
17 |
3.439 |
3.556 |
2.444 |
4.125 |
0.568 |
0.138 |
0.292 |
1.000 |
YES |
-0.225 |
-1.571 |
| Experimental |
M |
|
|
|
fss.media.read |
29 |
3.467 |
3.444 |
1.889 |
4.667 |
0.589 |
0.109 |
0.224 |
0.778 |
YES |
-0.252 |
0.160 |
| Controle |
|
Rural |
|
|
dfs.media.read |
55 |
3.307 |
3.333 |
2.111 |
4.444 |
0.549 |
0.074 |
0.148 |
0.722 |
YES |
-0.010 |
-0.602 |
| Controle |
|
Urbana |
|
|
dfs.media.read |
18 |
3.241 |
3.333 |
2.556 |
3.778 |
0.380 |
0.089 |
0.189 |
0.500 |
NO |
-0.652 |
-0.810 |
| Experimental |
|
Rural |
|
|
dfs.media.read |
29 |
3.531 |
3.556 |
2.333 |
4.333 |
0.550 |
0.102 |
0.209 |
0.778 |
YES |
-0.368 |
-0.689 |
| Experimental |
|
Urbana |
|
|
dfs.media.read |
8 |
3.042 |
3.167 |
2.111 |
3.556 |
0.493 |
0.174 |
0.412 |
0.472 |
NO |
-0.699 |
-1.063 |
| Controle |
|
Rural |
|
|
fss.media.read |
55 |
3.528 |
3.444 |
2.556 |
4.778 |
0.446 |
0.060 |
0.121 |
0.500 |
NO |
0.529 |
0.255 |
| Controle |
|
Urbana |
|
|
fss.media.read |
18 |
3.420 |
3.278 |
2.222 |
5.000 |
0.769 |
0.181 |
0.382 |
1.122 |
YES |
0.372 |
-0.958 |
| Experimental |
|
Rural |
|
|
fss.media.read |
29 |
3.472 |
3.556 |
1.889 |
4.667 |
0.630 |
0.117 |
0.239 |
0.889 |
YES |
-0.393 |
-0.270 |
| Experimental |
|
Urbana |
|
|
fss.media.read |
8 |
3.431 |
3.444 |
2.778 |
4.111 |
0.509 |
0.180 |
0.425 |
0.861 |
YES |
0.024 |
-1.815 |
| Controle |
|
|
Parda |
|
dfs.media.read |
46 |
3.451 |
3.500 |
2.444 |
4.444 |
0.457 |
0.067 |
0.136 |
0.444 |
YES |
-0.028 |
-0.392 |
| Controle |
|
|
Branca |
|
dfs.media.read |
11 |
3.202 |
3.333 |
2.111 |
4.222 |
0.618 |
0.186 |
0.415 |
0.500 |
YES |
-0.228 |
-0.922 |
| Experimental |
|
|
Parda |
|
dfs.media.read |
12 |
3.479 |
3.444 |
2.444 |
4.333 |
0.523 |
0.151 |
0.332 |
0.618 |
YES |
-0.146 |
-0.774 |
| Experimental |
|
|
Branca |
|
dfs.media.read |
6 |
3.611 |
3.778 |
2.778 |
4.333 |
0.599 |
0.245 |
0.629 |
0.750 |
YES |
-0.280 |
-1.814 |
| Controle |
|
|
Parda |
|
fss.media.read |
46 |
3.514 |
3.444 |
2.444 |
5.000 |
0.530 |
0.078 |
0.157 |
0.628 |
NO |
0.545 |
0.343 |
| Controle |
|
|
Branca |
|
fss.media.read |
11 |
3.596 |
3.556 |
2.667 |
4.556 |
0.505 |
0.152 |
0.339 |
0.333 |
YES |
0.140 |
-0.470 |
| Experimental |
|
|
Parda |
|
fss.media.read |
12 |
3.278 |
3.278 |
1.889 |
4.444 |
0.650 |
0.188 |
0.413 |
0.722 |
YES |
-0.325 |
-0.255 |
| Experimental |
|
|
Branca |
|
fss.media.read |
6 |
3.556 |
3.556 |
3.000 |
4.222 |
0.410 |
0.167 |
0.430 |
0.306 |
YES |
0.279 |
-1.252 |
| Controle |
|
|
|
6 ano |
dfs.media.read |
32 |
3.156 |
3.222 |
2.111 |
4.444 |
0.561 |
0.099 |
0.202 |
0.750 |
YES |
0.203 |
-0.450 |
| Controle |
|
|
|
7 ano |
dfs.media.read |
33 |
3.420 |
3.444 |
2.750 |
4.000 |
0.302 |
0.053 |
0.107 |
0.444 |
YES |
-0.315 |
-0.571 |
| Controle |
|
|
|
8 ano |
dfs.media.read |
17 |
3.268 |
3.222 |
2.556 |
4.333 |
0.445 |
0.108 |
0.229 |
0.556 |
NO |
0.613 |
-0.214 |
| Controle |
|
|
|
9 ano |
dfs.media.read |
19 |
3.381 |
3.333 |
2.444 |
4.222 |
0.444 |
0.102 |
0.214 |
0.500 |
YES |
0.152 |
-0.548 |
| Experimental |
|
|
|
6 ano |
dfs.media.read |
15 |
3.619 |
3.444 |
3.000 |
4.333 |
0.453 |
0.117 |
0.251 |
0.722 |
YES |
0.144 |
-1.548 |
| Experimental |
|
|
|
7 ano |
dfs.media.read |
11 |
3.485 |
3.444 |
2.778 |
4.333 |
0.571 |
0.172 |
0.384 |
0.944 |
YES |
0.238 |
-1.575 |
| Experimental |
|
|
|
8 ano |
dfs.media.read |
11 |
3.121 |
3.333 |
2.111 |
4.000 |
0.656 |
0.198 |
0.441 |
1.111 |
YES |
-0.222 |
-1.662 |
| Experimental |
|
|
|
9 ano |
dfs.media.read |
9 |
3.358 |
3.556 |
2.778 |
3.889 |
0.426 |
0.142 |
0.327 |
0.667 |
YES |
-0.246 |
-1.764 |
| Controle |
|
|
|
6 ano |
fss.media.read |
32 |
3.521 |
3.556 |
2.222 |
4.556 |
0.465 |
0.082 |
0.168 |
0.361 |
YES |
-0.132 |
0.785 |
| Controle |
|
|
|
7 ano |
fss.media.read |
33 |
3.546 |
3.444 |
2.444 |
5.000 |
0.573 |
0.100 |
0.203 |
0.653 |
NO |
0.567 |
0.138 |
| Controle |
|
|
|
8 ano |
fss.media.read |
17 |
3.273 |
3.333 |
2.500 |
4.000 |
0.477 |
0.116 |
0.245 |
0.556 |
YES |
0.026 |
-1.123 |
| Controle |
|
|
|
9 ano |
fss.media.read |
19 |
3.497 |
3.444 |
2.778 |
4.222 |
0.410 |
0.094 |
0.197 |
0.444 |
YES |
0.234 |
-0.930 |
| Experimental |
|
|
|
6 ano |
fss.media.read |
15 |
3.674 |
3.667 |
3.000 |
4.667 |
0.469 |
0.121 |
0.259 |
0.722 |
YES |
0.255 |
-0.900 |
| Experimental |
|
|
|
7 ano |
fss.media.read |
11 |
3.395 |
3.556 |
1.889 |
4.444 |
0.808 |
0.244 |
0.543 |
1.174 |
YES |
-0.427 |
-1.227 |
| Experimental |
|
|
|
8 ano |
fss.media.read |
11 |
3.222 |
3.000 |
2.778 |
4.222 |
0.463 |
0.140 |
0.311 |
0.556 |
NO |
0.857 |
-0.606 |
| Experimental |
|
|
|
9 ano |
fss.media.read |
9 |
3.457 |
3.556 |
2.667 |
4.000 |
0.473 |
0.158 |
0.363 |
0.778 |
YES |
-0.336 |
-1.525 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
dfs.media.read |
1 |
144 |
16.860 |
0.000 |
* |
0.105 |
1 |
144 |
16.860 |
0.000 |
* |
0.105 |
| 2 |
grupo |
1 |
144 |
0.542 |
0.463 |
|
0.004 |
1 |
144 |
0.542 |
0.463 |
|
0.004 |
| 5 |
grupo:Sexo |
1 |
142 |
0.029 |
0.864 |
|
0.000 |
1 |
142 |
0.029 |
0.864 |
|
0.000 |
| 6 |
Sexo |
1 |
142 |
0.726 |
0.396 |
|
0.005 |
1 |
142 |
0.726 |
0.396 |
|
0.005 |
| 9 |
grupo:Zona |
1 |
105 |
0.694 |
0.407 |
|
0.007 |
1 |
105 |
0.694 |
0.407 |
|
0.007 |
| 10 |
Zona |
1 |
105 |
0.023 |
0.879 |
|
0.000 |
1 |
105 |
0.023 |
0.879 |
|
0.000 |
| 11 |
Cor.Raca |
1 |
70 |
1.724 |
0.193 |
|
0.024 |
1 |
70 |
1.724 |
0.193 |
|
0.024 |
| 14 |
grupo:Cor.Raca |
1 |
70 |
0.040 |
0.843 |
|
0.001 |
1 |
70 |
0.040 |
0.843 |
|
0.001 |
| 17 |
grupo:Serie |
3 |
138 |
0.224 |
0.880 |
|
0.005 |
3 |
138 |
0.224 |
0.880 |
|
0.005 |
| 18 |
Serie |
3 |
138 |
1.871 |
0.137 |
|
0.039 |
3 |
138 |
1.871 |
0.137 |
|
0.039 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
pre |
pos |
290 |
-2.525 |
0.012 |
0.012 |
* |
290 |
-2.525 |
0.012 |
0.012 |
* |
| Experimental |
|
|
|
|
pre |
pos |
290 |
-0.382 |
0.703 |
0.703 |
ns |
290 |
-0.382 |
0.703 |
0.703 |
ns |
|
|
|
|
|
Controle |
Experimental |
144 |
0.737 |
0.463 |
0.463 |
ns |
144 |
0.737 |
0.463 |
0.463 |
ns |
| Controle |
F |
|
|
|
pre |
pos |
286 |
-2.319 |
0.021 |
0.021 |
* |
286 |
-2.319 |
0.021 |
0.021 |
* |
| Controle |
M |
|
|
|
pre |
pos |
286 |
-1.322 |
0.187 |
0.187 |
ns |
286 |
-1.322 |
0.187 |
0.187 |
ns |
| Controle |
|
|
|
|
F |
M |
142 |
0.629 |
0.530 |
0.530 |
ns |
142 |
0.629 |
0.530 |
0.530 |
ns |
| Experimental |
F |
|
|
|
pre |
pos |
286 |
-1.368 |
0.172 |
0.172 |
ns |
286 |
-1.368 |
0.172 |
0.172 |
ns |
| Experimental |
M |
|
|
|
pre |
pos |
286 |
0.565 |
0.572 |
0.572 |
ns |
286 |
0.565 |
0.572 |
0.572 |
ns |
| Experimental |
|
|
|
|
F |
M |
142 |
0.609 |
0.544 |
0.544 |
ns |
142 |
0.609 |
0.544 |
0.544 |
ns |
|
F |
|
|
|
Controle |
Experimental |
142 |
0.299 |
0.766 |
0.766 |
ns |
142 |
0.299 |
0.766 |
0.766 |
ns |
|
M |
|
|
|
Controle |
Experimental |
142 |
0.640 |
0.523 |
0.523 |
ns |
142 |
0.640 |
0.523 |
0.523 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
105 |
0.584 |
0.561 |
0.561 |
ns |
105 |
0.584 |
0.561 |
0.561 |
ns |
| Controle |
|
Rural |
|
|
pre |
pos |
212 |
-2.138 |
0.034 |
0.034 |
* |
212 |
-2.138 |
0.034 |
0.034 |
* |
| Controle |
|
Urbana |
|
|
pre |
pos |
212 |
-0.989 |
0.324 |
0.324 |
ns |
212 |
-0.989 |
0.324 |
0.324 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
105 |
-0.608 |
0.545 |
0.545 |
ns |
105 |
-0.608 |
0.545 |
0.545 |
ns |
| Experimental |
|
Rural |
|
|
pre |
pos |
212 |
0.413 |
0.680 |
0.680 |
ns |
212 |
0.413 |
0.680 |
0.680 |
ns |
| Experimental |
|
Urbana |
|
|
pre |
pos |
212 |
-1.432 |
0.154 |
0.154 |
ns |
212 |
-1.432 |
0.154 |
0.154 |
ns |
|
|
Rural |
|
|
Controle |
Experimental |
105 |
1.092 |
0.277 |
0.277 |
ns |
105 |
1.092 |
0.277 |
0.277 |
ns |
|
|
Urbana |
|
|
Controle |
Experimental |
105 |
-0.359 |
0.721 |
0.721 |
ns |
105 |
-0.359 |
0.721 |
0.721 |
ns |
| Controle |
|
|
Branca |
|
pre |
pos |
142 |
-1.770 |
0.079 |
0.079 |
ns |
142 |
-1.770 |
0.079 |
0.079 |
ns |
| Controle |
|
|
|
|
Parda |
Branca |
70 |
-0.967 |
0.337 |
0.337 |
ns |
70 |
-0.967 |
0.337 |
0.337 |
ns |
| Controle |
|
|
Parda |
|
pre |
pos |
142 |
-0.583 |
0.561 |
0.561 |
ns |
142 |
-0.583 |
0.561 |
0.561 |
ns |
| Experimental |
|
|
Branca |
|
pre |
pos |
142 |
0.184 |
0.854 |
0.854 |
ns |
142 |
0.184 |
0.854 |
0.854 |
ns |
| Experimental |
|
|
|
|
Parda |
Branca |
70 |
-0.900 |
0.371 |
0.371 |
ns |
70 |
-0.900 |
0.371 |
0.371 |
ns |
| Experimental |
|
|
Parda |
|
pre |
pos |
142 |
0.945 |
0.346 |
0.346 |
ns |
142 |
0.945 |
0.346 |
0.346 |
ns |
|
|
|
Branca |
|
Controle |
Experimental |
70 |
0.696 |
0.489 |
0.489 |
ns |
70 |
0.696 |
0.489 |
0.489 |
ns |
|
|
|
Parda |
|
Controle |
Experimental |
70 |
1.482 |
0.143 |
0.143 |
ns |
70 |
1.482 |
0.143 |
0.143 |
ns |
| Controle |
|
|
|
6 ano |
pre |
pos |
278 |
-2.934 |
0.004 |
0.004 |
** |
278 |
-2.934 |
0.004 |
0.004 |
** |
| Controle |
|
|
|
7 ano |
pre |
pos |
278 |
-1.029 |
0.305 |
0.305 |
ns |
278 |
-1.029 |
0.305 |
0.305 |
ns |
| Controle |
|
|
|
8 ano |
pre |
pos |
278 |
-0.029 |
0.977 |
0.977 |
ns |
278 |
-0.029 |
0.977 |
0.977 |
ns |
| Controle |
|
|
|
9 ano |
pre |
pos |
278 |
-0.721 |
0.472 |
0.472 |
ns |
278 |
-0.721 |
0.472 |
0.472 |
ns |
| Controle |
|
|
|
|
6 ano |
7 ano |
138 |
0.496 |
0.621 |
1.000 |
ns |
138 |
0.496 |
0.621 |
1.000 |
ns |
| Controle |
|
|
|
|
6 ano |
8 ano |
138 |
1.909 |
0.058 |
0.350 |
ns |
138 |
1.909 |
0.058 |
0.350 |
ns |
| Controle |
|
|
|
|
6 ano |
9 ano |
138 |
0.676 |
0.500 |
1.000 |
ns |
138 |
0.676 |
0.500 |
1.000 |
ns |
| Controle |
|
|
|
|
7 ano |
8 ano |
138 |
1.498 |
0.137 |
0.819 |
ns |
138 |
1.498 |
0.137 |
0.819 |
ns |
| Controle |
|
|
|
|
7 ano |
9 ano |
138 |
0.251 |
0.802 |
1.000 |
ns |
138 |
0.251 |
0.802 |
1.000 |
ns |
| Controle |
|
|
|
|
8 ano |
9 ano |
138 |
-1.126 |
0.262 |
1.000 |
ns |
138 |
-1.126 |
0.262 |
1.000 |
ns |
| Experimental |
|
|
|
6 ano |
pre |
pos |
278 |
-0.306 |
0.760 |
0.760 |
ns |
278 |
-0.306 |
0.760 |
0.760 |
ns |
| Experimental |
|
|
|
7 ano |
pre |
pos |
278 |
0.423 |
0.673 |
0.673 |
ns |
278 |
0.423 |
0.673 |
0.673 |
ns |
| Experimental |
|
|
|
8 ano |
pre |
pos |
278 |
-0.477 |
0.634 |
0.634 |
ns |
278 |
-0.477 |
0.634 |
0.634 |
ns |
| Experimental |
|
|
|
9 ano |
pre |
pos |
278 |
-0.422 |
0.674 |
0.674 |
ns |
278 |
-0.422 |
0.674 |
0.674 |
ns |
| Experimental |
|
|
|
|
6 ano |
7 ano |
138 |
1.190 |
0.236 |
1.000 |
ns |
138 |
1.190 |
0.236 |
1.000 |
ns |
| Experimental |
|
|
|
|
6 ano |
8 ano |
138 |
1.424 |
0.157 |
0.940 |
ns |
138 |
1.424 |
0.157 |
0.940 |
ns |
| Experimental |
|
|
|
|
6 ano |
9 ano |
138 |
0.623 |
0.534 |
1.000 |
ns |
138 |
0.623 |
0.534 |
1.000 |
ns |
| Experimental |
|
|
|
|
7 ano |
8 ano |
138 |
0.247 |
0.806 |
1.000 |
ns |
138 |
0.247 |
0.806 |
1.000 |
ns |
| Experimental |
|
|
|
|
7 ano |
9 ano |
138 |
-0.464 |
0.644 |
1.000 |
ns |
138 |
-0.464 |
0.644 |
1.000 |
ns |
| Experimental |
|
|
|
|
8 ano |
9 ano |
138 |
-0.698 |
0.486 |
1.000 |
ns |
138 |
-0.698 |
0.486 |
1.000 |
ns |
|
|
|
|
6 ano |
Controle |
Experimental |
138 |
-0.003 |
0.998 |
0.998 |
ns |
138 |
-0.003 |
0.998 |
0.998 |
ns |
|
|
|
|
7 ano |
Controle |
Experimental |
138 |
0.996 |
0.321 |
0.321 |
ns |
138 |
0.996 |
0.321 |
0.321 |
ns |
|
|
|
|
8 ano |
Controle |
Experimental |
138 |
0.011 |
0.991 |
0.991 |
ns |
138 |
0.011 |
0.991 |
0.991 |
ns |
|
|
|
|
9 ano |
Controle |
Experimental |
138 |
0.163 |
0.871 |
0.871 |
ns |
138 |
0.163 |
0.871 |
0.871 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
101 |
3.303 |
0.045 |
3.483 |
0.050 |
3.495 |
0.050 |
3.397 |
3.593 |
101 |
3.303 |
0.045 |
3.483 |
0.050 |
3.495 |
0.050 |
3.397 |
3.593 |
0 |
| Experimental |
|
|
|
|
46 |
3.417 |
0.081 |
3.457 |
0.085 |
3.430 |
0.074 |
3.284 |
3.575 |
46 |
3.417 |
0.081 |
3.457 |
0.085 |
3.430 |
0.074 |
3.284 |
3.575 |
0 |
| Controle |
F |
|
|
|
45 |
3.254 |
0.065 |
3.500 |
0.072 |
3.531 |
0.075 |
3.383 |
3.678 |
45 |
3.254 |
0.065 |
3.500 |
0.072 |
3.531 |
0.075 |
3.383 |
3.678 |
0 |
| Controle |
M |
|
|
|
56 |
3.344 |
0.063 |
3.469 |
0.069 |
3.468 |
0.067 |
3.336 |
3.599 |
56 |
3.344 |
0.063 |
3.469 |
0.069 |
3.468 |
0.067 |
3.336 |
3.599 |
0 |
| Experimental |
F |
|
|
|
17 |
3.203 |
0.145 |
3.439 |
0.138 |
3.488 |
0.122 |
3.248 |
3.728 |
17 |
3.203 |
0.145 |
3.439 |
0.138 |
3.488 |
0.122 |
3.248 |
3.728 |
0 |
| Experimental |
M |
|
|
|
29 |
3.542 |
0.090 |
3.467 |
0.109 |
3.394 |
0.094 |
3.207 |
3.580 |
29 |
3.542 |
0.090 |
3.467 |
0.109 |
3.394 |
0.094 |
3.207 |
3.580 |
0 |
| Controle |
|
Rural |
|
|
55 |
3.307 |
0.074 |
3.528 |
0.060 |
3.538 |
0.072 |
3.395 |
3.681 |
55 |
3.307 |
0.074 |
3.528 |
0.060 |
3.538 |
0.072 |
3.395 |
3.681 |
0 |
| Controle |
|
Urbana |
|
|
18 |
3.241 |
0.089 |
3.420 |
0.181 |
3.454 |
0.126 |
3.203 |
3.704 |
18 |
3.241 |
0.089 |
3.420 |
0.181 |
3.454 |
0.126 |
3.203 |
3.704 |
0 |
| Experimental |
|
Rural |
|
|
29 |
3.531 |
0.102 |
3.472 |
0.117 |
3.402 |
0.101 |
3.202 |
3.603 |
29 |
3.531 |
0.102 |
3.472 |
0.117 |
3.402 |
0.101 |
3.202 |
3.603 |
0 |
| Experimental |
|
Urbana |
|
|
8 |
3.042 |
0.174 |
3.431 |
0.180 |
3.535 |
0.191 |
3.156 |
3.915 |
8 |
3.042 |
0.174 |
3.431 |
0.180 |
3.535 |
0.191 |
3.156 |
3.915 |
0 |
| Controle |
|
|
Branca |
|
11 |
3.202 |
0.186 |
3.596 |
0.152 |
3.677 |
0.157 |
3.363 |
3.991 |
11 |
3.202 |
0.186 |
3.596 |
0.152 |
3.677 |
0.157 |
3.363 |
3.991 |
0 |
| Controle |
|
|
Parda |
|
46 |
3.451 |
0.067 |
3.514 |
0.078 |
3.508 |
0.076 |
3.357 |
3.659 |
46 |
3.451 |
0.067 |
3.514 |
0.078 |
3.508 |
0.076 |
3.357 |
3.659 |
0 |
| Experimental |
|
|
Branca |
|
6 |
3.611 |
0.245 |
3.556 |
0.167 |
3.492 |
0.211 |
3.072 |
3.913 |
6 |
3.611 |
0.245 |
3.556 |
0.167 |
3.492 |
0.211 |
3.072 |
3.913 |
0 |
| Experimental |
|
|
Parda |
|
12 |
3.479 |
0.151 |
3.278 |
0.188 |
3.261 |
0.148 |
2.965 |
3.557 |
12 |
3.479 |
0.151 |
3.278 |
0.188 |
3.261 |
0.148 |
2.965 |
3.557 |
0 |
| Controle |
|
|
|
6 ano |
32 |
3.156 |
0.099 |
3.521 |
0.082 |
3.581 |
0.089 |
3.405 |
3.758 |
32 |
3.156 |
0.099 |
3.521 |
0.082 |
3.581 |
0.089 |
3.405 |
3.758 |
0 |
| Controle |
|
|
|
7 ano |
33 |
3.420 |
0.053 |
3.546 |
0.100 |
3.519 |
0.087 |
3.348 |
3.690 |
33 |
3.420 |
0.053 |
3.546 |
0.100 |
3.519 |
0.087 |
3.348 |
3.690 |
0 |
| Controle |
|
|
|
8 ano |
17 |
3.268 |
0.108 |
3.273 |
0.116 |
3.296 |
0.120 |
3.058 |
3.535 |
17 |
3.268 |
0.108 |
3.273 |
0.116 |
3.296 |
0.120 |
3.058 |
3.535 |
0 |
| Controle |
|
|
|
9 ano |
19 |
3.381 |
0.102 |
3.497 |
0.094 |
3.483 |
0.114 |
3.258 |
3.708 |
19 |
3.381 |
0.102 |
3.497 |
0.094 |
3.483 |
0.114 |
3.258 |
3.708 |
0 |
| Experimental |
|
|
|
6 ano |
15 |
3.619 |
0.117 |
3.674 |
0.121 |
3.582 |
0.130 |
3.324 |
3.840 |
15 |
3.619 |
0.117 |
3.674 |
0.121 |
3.582 |
0.130 |
3.324 |
3.840 |
0 |
| Experimental |
|
|
|
7 ano |
11 |
3.485 |
0.172 |
3.395 |
0.244 |
3.347 |
0.150 |
3.050 |
3.644 |
11 |
3.485 |
0.172 |
3.395 |
0.244 |
3.347 |
0.150 |
3.050 |
3.644 |
0 |
| Experimental |
|
|
|
8 ano |
11 |
3.121 |
0.198 |
3.222 |
0.140 |
3.294 |
0.151 |
2.996 |
3.592 |
11 |
3.121 |
0.198 |
3.222 |
0.140 |
3.294 |
0.151 |
2.996 |
3.592 |
0 |
| Experimental |
|
|
|
9 ano |
9 |
3.358 |
0.142 |
3.457 |
0.158 |
3.450 |
0.165 |
3.123 |
3.777 |
9 |
3.358 |
0.142 |
3.457 |
0.158 |
3.450 |
0.165 |
3.123 |
3.777 |
0 |